Overview

Dataset statistics

Number of variables35
Number of observations69929
Missing cells69930
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.5 MiB
Average record size in memory1.1 KiB

Variable types

CAT18
NUM16
UNSUPPORTED1

Warnings

YEAR has constant value "69929" Constant
UNIQUE_CARRIER has a high cardinality: 266 distinct values High cardinality
UNIQUE_CARRIER_NAME has a high cardinality: 266 distinct values High cardinality
UNIQUE_CARRIER_ENTITY has a high cardinality: 304 distinct values High cardinality
CARRIER has a high cardinality: 267 distinct values High cardinality
CARRIER_NAME has a high cardinality: 268 distinct values High cardinality
ORIGIN has a high cardinality: 868 distinct values High cardinality
ORIGIN_CITY_NAME has a high cardinality: 786 distinct values High cardinality
ORIGIN_COUNTRY has a high cardinality: 135 distinct values High cardinality
ORIGIN_COUNTRY_NAME has a high cardinality: 136 distinct values High cardinality
DEST has a high cardinality: 876 distinct values High cardinality
DEST_CITY_NAME has a high cardinality: 790 distinct values High cardinality
DEST_COUNTRY has a high cardinality: 146 distinct values High cardinality
DEST_COUNTRY_NAME has a high cardinality: 146 distinct values High cardinality
ORIGIN_AIRPORT_SEQ_ID is highly correlated with ORIGIN_AIRPORT_IDHigh correlation
ORIGIN_AIRPORT_ID is highly correlated with ORIGIN_AIRPORT_SEQ_IDHigh correlation
DEST_AIRPORT_SEQ_ID is highly correlated with DEST_AIRPORT_IDHigh correlation
DEST_AIRPORT_ID is highly correlated with DEST_AIRPORT_SEQ_IDHigh correlation
MONTH is highly correlated with QUARTERHigh correlation
QUARTER is highly correlated with MONTHHigh correlation
DISTANCE_GROUP is highly correlated with DISTANCEHigh correlation
DISTANCE is highly correlated with DISTANCE_GROUPHigh correlation
Unnamed: 34 has 69929 (100.0%) missing values Missing
Unnamed: 34 is an unsupported type, check if it needs cleaning or further analysis Unsupported
PASSENGERS has 13401 (19.2%) zeros Zeros
FREIGHT has 33932 (48.5%) zeros Zeros
MAIL has 63875 (91.3%) zeros Zeros
CARRIER_GROUP_NEW has 28525 (40.8%) zeros Zeros

Reproduction

Analysis started2022-01-09 19:34:42.359536
Analysis finished2022-01-09 19:35:14.025255
Duration31.67 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

PASSENGERS
Real number (ℝ≥0)

ZEROS

Distinct12916
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3062.016359
Minimum0
Maximum61804
Zeros13401
Zeros (%)19.2%
Memory size546.4 KiB
2022-01-09T13:35:14.107573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median1190
Q34650
95-th percentile11420
Maximum61804
Range61804
Interquartile range (IQR)4639

Descriptive statistics

Standard deviation4444.937626
Coefficient of variation (CV)1.451637452
Kurtosis14.51531621
Mean3062.016359
Median Absolute Deviation (MAD)1190
Skewness2.871505168
Sum214123742
Variance19757470.5
MonotocityIncreasing
2022-01-09T13:35:14.190105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01340119.2%
 
28231.2%
 
16781.0%
 
44940.7%
 
34750.7%
 
53520.5%
 
63400.5%
 
82600.4%
 
72410.3%
 
91910.3%
 
101500.2%
 
111100.2%
 
481100.2%
 
121060.2%
 
451000.1%
 
47880.1%
 
50870.1%
 
13870.1%
 
15870.1%
 
49860.1%
 
46790.1%
 
14780.1%
 
43760.1%
 
51720.1%
 
42670.1%
 
Other values (12891)5129173.3%
 
ValueCountFrequency (%) 
01340119.2%
 
16781.0%
 
28231.2%
 
34750.7%
 
44940.7%
 
53520.5%
 
63400.5%
 
72410.3%
 
82600.4%
 
91910.3%
 
ValueCountFrequency (%) 
618041< 0.1%
 
610571< 0.1%
 
604851< 0.1%
 
597751< 0.1%
 
581921< 0.1%
 
581081< 0.1%
 
574041< 0.1%
 
569951< 0.1%
 
567841< 0.1%
 
564631< 0.1%
 

FREIGHT
Real number (ℝ≥0)

ZEROS

Distinct31690
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274477.5046
Minimum0
Maximum13844061
Zeros33932
Zeros (%)48.5%
Memory size546.4 KiB
2022-01-09T13:35:14.299453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median121
Q3195584
95-th percentile1430669.4
Maximum13844061
Range13844061
Interquartile range (IQR)195584

Descriptive statistics

Standard deviation761085.906
Coefficient of variation (CV)2.772853488
Kurtosis47.30320229
Mean274477.5046
Median Absolute Deviation (MAD)121
Skewness5.730789853
Sum1.919393742e+10
Variance5.792517563e+11
MonotocityNot monotonic
2022-01-09T13:35:14.393180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03393248.5%
 
1490.1%
 
2440.1%
 
22028< 0.1%
 
427< 0.1%
 
2624< 0.1%
 
1121< 0.1%
 
3319< 0.1%
 
319< 0.1%
 
2219< 0.1%
 
15019< 0.1%
 
3018< 0.1%
 
918< 0.1%
 
3117< 0.1%
 
55017< 0.1%
 
11017< 0.1%
 
4617< 0.1%
 
717< 0.1%
 
5117< 0.1%
 
4416< 0.1%
 
3516< 0.1%
 
5515< 0.1%
 
1815< 0.1%
 
2514< 0.1%
 
4014< 0.1%
 
Other values (31665)3550050.8%
 
ValueCountFrequency (%) 
03393248.5%
 
1490.1%
 
2440.1%
 
319< 0.1%
 
427< 0.1%
 
59< 0.1%
 
614< 0.1%
 
717< 0.1%
 
814< 0.1%
 
918< 0.1%
 
ValueCountFrequency (%) 
138440611< 0.1%
 
136650051< 0.1%
 
131712941< 0.1%
 
121986221< 0.1%
 
121088181< 0.1%
 
120947901< 0.1%
 
116696541< 0.1%
 
114945641< 0.1%
 
114588301< 0.1%
 
113795351< 0.1%
 

MAIL
Real number (ℝ≥0)

ZEROS

Distinct5254
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3109.367258
Minimum0
Maximum779915
Zeros63875
Zeros (%)91.3%
Memory size546.4 KiB
2022-01-09T13:35:14.488222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8069
Maximum779915
Range779915
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20630.39567
Coefficient of variation (CV)6.63491764
Kurtosis202.6815452
Mean3109.367258
Median Absolute Deviation (MAD)0
Skewness12.13475072
Sum217434943
Variance425613225.6
MonotocityNot monotonic
2022-01-09T13:35:14.596510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06387591.3%
 
1370.1%
 
228< 0.1%
 
416< 0.1%
 
1215< 0.1%
 
515< 0.1%
 
1014< 0.1%
 
612< 0.1%
 
3012< 0.1%
 
2012< 0.1%
 
1111< 0.1%
 
811< 0.1%
 
2811< 0.1%
 
1910< 0.1%
 
710< 0.1%
 
229< 0.1%
 
249< 0.1%
 
38< 0.1%
 
138< 0.1%
 
268< 0.1%
 
297< 0.1%
 
97< 0.1%
 
237< 0.1%
 
447< 0.1%
 
257< 0.1%
 
Other values (5229)57638.2%
 
ValueCountFrequency (%) 
06387591.3%
 
1370.1%
 
228< 0.1%
 
38< 0.1%
 
416< 0.1%
 
515< 0.1%
 
612< 0.1%
 
710< 0.1%
 
811< 0.1%
 
97< 0.1%
 
ValueCountFrequency (%) 
7799151< 0.1%
 
5653371< 0.1%
 
5571891< 0.1%
 
5108511< 0.1%
 
5107671< 0.1%
 
4893081< 0.1%
 
4726711< 0.1%
 
4620011< 0.1%
 
4601251< 0.1%
 
4474461< 0.1%
 

DISTANCE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3404
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2859.55083
Minimum17
Maximum10503
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:14.690241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile319
Q11101
median1927
Q34427
95-th percentile7247
Maximum10503
Range10486
Interquartile range (IQR)3326

Descriptive statistics

Standard deviation2240.010511
Coefficient of variation (CV)0.7833434842
Kurtosis-0.3687139621
Mean2859.55083
Median Absolute Deviation (MAD)1414
Skewness0.7996518652
Sum199965530
Variance5017647.088
MonotocityNot monotonic
2022-01-09T13:35:14.783965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15063450.5%
 
11172870.4%
 
13882860.4%
 
15542720.4%
 
10172350.3%
 
9112160.3%
 
4362150.3%
 
64852120.3%
 
17872090.3%
 
15532080.3%
 
13072040.3%
 
59952040.3%
 
26082000.3%
 
8661980.3%
 
41181900.3%
 
38191840.3%
 
11521830.3%
 
10811800.3%
 
12761800.3%
 
70571760.3%
 
54511760.3%
 
3241750.3%
 
7481750.3%
 
65521740.2%
 
4531680.2%
 
Other values (3379)6467792.5%
 
ValueCountFrequency (%) 
175< 0.1%
 
182< 0.1%
 
199< 0.1%
 
202< 0.1%
 
214< 0.1%
 
2918< 0.1%
 
309< 0.1%
 
361< 0.1%
 
372< 0.1%
 
405< 0.1%
 
ValueCountFrequency (%) 
105031< 0.1%
 
102061< 0.1%
 
1020124< 0.1%
 
101051< 0.1%
 
986611< 0.1%
 
9853600.1%
 
983824< 0.1%
 
96711< 0.1%
 
964224< 0.1%
 
959511< 0.1%
 

UNIQUE_CARRIER
Categorical

HIGH CARDINALITY

Distinct266
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
AA
7545 
DL
6757 
UA
6562 
B6
 
1657
WS
 
1342
Other values (261)
46066 
ValueCountFrequency (%) 
AA754510.8%
 
DL67579.7%
 
UA65629.4%
 
B616572.4%
 
WS13421.9%
 
Y411421.6%
 
5X9681.4%
 
AC9471.4%
 
LH9461.4%
 
EV9361.3%
 
KE9241.3%
 
5Y9051.3%
 
OO9021.3%
 
WN8701.2%
 
FX8531.2%
 
09Q7641.1%
 
NK7171.0%
 
AM6781.0%
 
DY6751.0%
 
YX6470.9%
 
VJT6410.9%
 
WL6250.9%
 
ABX6130.9%
 
13Q6110.9%
 
CV6100.9%
 
Other values (241)3009243.0%
 
2022-01-09T13:35:14.893316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2022-01-09T13:35:14.987043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.099000415
Min length2

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A2925519.9%
 
L107927.4%
 
D83825.7%
 
U79085.4%
 
Q72164.9%
 
X52553.6%
 
K50023.4%
 
V48773.3%
 
S48203.3%
 
Y47543.2%
 
W44553.0%
 
B43663.0%
 
E42632.9%
 
C37042.5%
 
M33732.3%
 
O31012.1%
 
F27971.9%
 
524441.7%
 
N23931.6%
 
423831.6%
 
623601.6%
 
R22681.5%
 
P21801.5%
 
H21631.5%
 
T19591.3%
 
Other values (11)143119.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter13161689.7%
 
Decimal Number1516510.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A2925522.2%
 
L107928.2%
 
D83826.4%
 
U79086.0%
 
Q72165.5%
 
X52554.0%
 
K50023.8%
 
V48773.7%
 
S48203.7%
 
Y47543.6%
 
W44553.4%
 
B43663.3%
 
E42633.2%
 
C37042.8%
 
M33732.6%
 
O31012.4%
 
F27972.1%
 
N23931.8%
 
R22681.7%
 
P21801.7%
 
H21631.6%
 
T19591.5%
 
J18501.4%
 
Z17241.3%
 
I16591.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
5244416.1%
 
4238315.7%
 
6236015.6%
 
9170511.2%
 
3161310.6%
 
0151910.0%
 
114409.5%
 
78965.9%
 
24392.9%
 
83662.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin13161689.7%
 
Common1516510.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A2925522.2%
 
L107928.2%
 
D83826.4%
 
U79086.0%
 
Q72165.5%
 
X52554.0%
 
K50023.8%
 
V48773.7%
 
S48203.7%
 
Y47543.6%
 
W44553.4%
 
B43663.3%
 
E42633.2%
 
C37042.8%
 
M33732.6%
 
O31012.4%
 
F27972.1%
 
N23931.8%
 
R22681.7%
 
P21801.7%
 
H21631.6%
 
T19591.5%
 
J18501.4%
 
Z17241.3%
 
I16591.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
5244416.1%
 
4238315.7%
 
6236015.6%
 
9170511.2%
 
3161310.6%
 
0151910.0%
 
114409.5%
 
78965.9%
 
24392.9%
 
83662.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII146781100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A2925519.9%
 
L107927.4%
 
D83825.7%
 
U79085.4%
 
Q72164.9%
 
X52553.6%
 
K50023.4%
 
V48773.3%
 
S48203.3%
 
Y47543.2%
 
W44553.0%
 
B43663.0%
 
E42632.9%
 
C37042.5%
 
M33732.3%
 
O31012.1%
 
F27971.9%
 
524441.7%
 
N23931.6%
 
423831.6%
 
623601.6%
 
R22681.5%
 
P21801.5%
 
H21631.5%
 
T19591.3%
 
Other values (11)143119.7%
 

AIRLINE_ID
Real number (ℝ≥0)

Distinct266
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20181.13793
Minimum19393
Maximum21814
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:15.065150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19393
5-th percentile19537
Q119790
median19977
Q320392
95-th percentile21578
Maximum21814
Range2421
Interquartile range (IQR)602

Descriptive statistics

Standard deviation609.9798074
Coefficient of variation (CV)0.03022524347
Kurtosis0.3525488294
Mean20181.13793
Median Absolute Deviation (MAD)300
Skewness1.171025665
Sum1411246794
Variance372075.3654
MonotocityNot monotonic
2022-01-09T13:35:15.158876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19805754510.8%
 
1979067579.7%
 
1997765629.4%
 
2040916572.4%
 
2022913421.9%
 
2135111421.6%
 
199179681.4%
 
195319471.4%
 
195549461.4%
 
203669361.3%
 
195509241.3%
 
200079051.3%
 
203049021.3%
 
193938701.2%
 
201078531.2%
 
211617641.1%
 
204167171.0%
 
195346781.0%
 
215796751.0%
 
204526470.9%
 
215686410.9%
 
203106250.9%
 
204536130.9%
 
214376110.9%
 
195456100.9%
 
Other values (241)3009243.0%
 
ValueCountFrequency (%) 
193938701.2%
 
195319471.4%
 
195324620.7%
 
195332020.3%
 
195346781.0%
 
19535580.1%
 
1953626< 0.1%
 
195373600.5%
 
19538650.1%
 
195391230.2%
 
ValueCountFrequency (%) 
218142< 0.1%
 
217851< 0.1%
 
217816< 0.1%
 
217802< 0.1%
 
2177410< 0.1%
 
217708< 0.1%
 
2176423< 0.1%
 
2176010< 0.1%
 
2174916< 0.1%
 
2173814< 0.1%
 

UNIQUE_CARRIER_NAME
Categorical

HIGH CARDINALITY

Distinct266
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
American Airlines Inc.
7545 
Delta Air Lines Inc.
6757 
United Air Lines Inc.
6562 
JetBlue Airways
 
1657
Westjet
 
1342
Other values (261)
46066 
ValueCountFrequency (%) 
American Airlines Inc.754510.8%
 
Delta Air Lines Inc.67579.7%
 
United Air Lines Inc.65629.4%
 
JetBlue Airways16572.4%
 
Westjet13421.9%
 
Concesionaria Vuela Compania De Aviacion SA de CV (Volaris)11421.6%
 
United Parcel Service9681.4%
 
Air Canada9471.4%
 
Lufthansa German Airlines9461.4%
 
ExpressJet Airlines LLC9361.3%
 
Korean Air Lines Co. Ltd.9241.3%
 
Atlas Air Inc.9051.3%
 
SkyWest Airlines Inc.9021.3%
 
Southwest Airlines Co.8701.2%
 
Federal Express Corporation8531.2%
 
Swift Air, LLC d/b/a Eastern Air Lines d/b/a Eastern7641.1%
 
Spirit Air Lines7171.0%
 
Aeromexico6781.0%
 
Norwegian Air Shuttle ASA6751.0%
 
Republic Airline6470.9%
 
VistaJet Limited6410.9%
 
Caribbean Sun Airlines, Inc. d/b/a World Atlantic Airlines6250.9%
 
ABX Air Inc6130.9%
 
Chartright Air Inc.6110.9%
 
Cargolux Airlines International S.A6100.9%
 
Other values (241)3009243.0%
 
2022-01-09T13:35:15.268225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2022-01-09T13:35:15.377574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length80
Median length21
Mean length22.12840166
Min length3

Overview of Unicode Properties

Unique unicode characters61
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
17285411.2%
 
i16738610.8%
 
n1344928.7%
 
e1221087.9%
 
r1126257.3%
 
a985976.4%
 
A848395.5%
 
s735844.8%
 
t610673.9%
 
l576623.7%
 
c495243.2%
 
o424262.7%
 
.383542.5%
 
I340952.2%
 
L319632.1%
 
d278521.8%
 
C241401.6%
 
m189091.2%
 
S149421.0%
 
u127930.8%
 
p125980.8%
 
y102150.7%
 
b94280.6%
 
w93130.6%
 
g91990.6%
 
Other values (36)1164527.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter106038368.5%
 
Uppercase Letter26150516.9%
 
Space Separator17285411.2%
 
Other Punctuation480213.1%
 
Open Punctuation18650.1%
 
Close Punctuation18650.1%
 
Dash Punctuation9240.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A8483932.4%
 
I3409513.0%
 
L3196312.2%
 
C241409.2%
 
S149425.7%
 
D88733.4%
 
U84693.2%
 
E73962.8%
 
V59162.3%
 
J52342.0%
 
P50901.9%
 
B48731.9%
 
W36521.4%
 
N33891.3%
 
G29611.1%
 
T28351.1%
 
F24130.9%
 
M21520.8%
 
K19350.7%
 
H16870.6%
 
R15940.6%
 
O10650.4%
 
Q8590.3%
 
X6690.3%
 
Y2830.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i16738615.8%
 
n13449212.7%
 
e12210811.5%
 
r11262510.6%
 
a985979.3%
 
s735846.9%
 
t610675.8%
 
l576625.4%
 
c495244.7%
 
o424264.0%
 
d278522.6%
 
m189091.8%
 
u127931.2%
 
p125981.2%
 
y102151.0%
 
b94280.9%
 
w93130.9%
 
g91990.9%
 
h88560.8%
 
v71610.7%
 
x37280.4%
 
f34370.3%
 
k33320.3%
 
j26460.2%
 
z14440.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
172854100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.3835479.9%
 
/726215.1%
 
,22654.7%
 
&920.2%
 
'480.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-924100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1865100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1865100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin132188885.4%
 
Common22552914.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i16738612.7%
 
n13449210.2%
 
e1221089.2%
 
r1126258.5%
 
a985977.5%
 
A848396.4%
 
s735845.6%
 
t610674.6%
 
l576624.4%
 
c495243.7%
 
o424263.2%
 
I340952.6%
 
L319632.4%
 
d278522.1%
 
C241401.8%
 
m189091.4%
 
S149421.1%
 
u127931.0%
 
p125981.0%
 
y102150.8%
 
b94280.7%
 
w93130.7%
 
g91990.7%
 
D88730.7%
 
h88560.7%
 
Other values (27)844026.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
17285476.6%
 
.3835417.0%
 
/72623.2%
 
,22651.0%
 
(18650.8%
 
)18650.8%
 
-9240.4%
 
&92< 0.1%
 
'48< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1547417100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
17285411.2%
 
i16738610.8%
 
n1344928.7%
 
e1221087.9%
 
r1126257.3%
 
a985976.4%
 
A848395.5%
 
s735844.8%
 
t610673.9%
 
l576623.7%
 
c495243.2%
 
o424262.7%
 
.383542.5%
 
I340952.2%
 
L319632.1%
 
d278521.8%
 
C241401.6%
 
m189091.2%
 
S149421.0%
 
u127930.8%
 
p125980.8%
 
y102150.7%
 
b94280.6%
 
w93130.6%
 
g91990.6%
 
Other values (36)1164527.5%
 

UNIQUE_CARRIER_ENTITY
Categorical

HIGH CARDINALITY

Distinct304
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
10050
 
4528
10876
 
3252
10260
 
2910
10261
 
2376
10049
 
2059
Other values (299)
54804 
ValueCountFrequency (%) 
1005045286.5%
 
1087632524.7%
 
1026029104.2%
 
1026123763.4%
 
1004920592.9%
 
1667316572.4%
 
1087414132.0%
 
1087713491.9%
 
9900Y13421.9%
 
7107211421.6%
 
1026210761.5%
 
9900A9471.4%
 
9429D9461.4%
 
9778A9241.3%
 
160879051.3%
 
110338701.2%
 
011307641.1%
 
9148B6781.0%
 
168316771.0%
 
711276751.0%
 
110296470.9%
 
711236410.9%
 
160766250.9%
 
067006190.9%
 
100526180.9%
 
Other values (279)3628951.9%
 
2022-01-09T13:35:15.491690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7 ?
Unique (%)< 0.1%
2022-01-09T13:35:15.788493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

Overview of Unicode Properties

Unique unicode characters29
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
07168420.5%
 
16779919.4%
 
93633510.4%
 
73486710.0%
 
6338279.7%
 
2211226.0%
 
8192005.5%
 
4158734.5%
 
3152754.4%
 
5127993.7%
 
A98702.8%
 
B37561.1%
 
D15220.4%
 
Y13420.4%
 
E10540.3%
 
G7340.2%
 
C6500.2%
 
L5030.1%
 
K3580.1%
 
W3560.1%
 
F2780.1%
 
H164< 0.1%
 
Z80< 0.1%
 
I62< 0.1%
 
P62< 0.1%
 
Other values (4)73< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number32878194.0%
 
Uppercase Letter208646.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
07168421.8%
 
16779920.6%
 
93633511.1%
 
73486710.6%
 
63382710.3%
 
2211226.4%
 
8192005.8%
 
4158734.8%
 
3152754.6%
 
5127993.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A987047.3%
 
B375618.0%
 
D15227.3%
 
Y13426.4%
 
E10545.1%
 
G7343.5%
 
C6503.1%
 
L5032.4%
 
K3581.7%
 
W3561.7%
 
F2781.3%
 
H1640.8%
 
Z800.4%
 
I620.3%
 
P620.3%
 
M240.1%
 
U240.1%
 
J240.1%
 
T1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common32878194.0%
 
Latin208646.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
07168421.8%
 
16779920.6%
 
93633511.1%
 
73486710.6%
 
63382710.3%
 
2211226.4%
 
8192005.8%
 
4158734.8%
 
3152754.6%
 
5127993.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A987047.3%
 
B375618.0%
 
D15227.3%
 
Y13426.4%
 
E10545.1%
 
G7343.5%
 
C6503.1%
 
L5032.4%
 
K3581.7%
 
W3561.7%
 
F2781.3%
 
H1640.8%
 
Z800.4%
 
I620.3%
 
P620.3%
 
M240.1%
 
U240.1%
 
J240.1%
 
T1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII349645100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
07168420.5%
 
16779919.4%
 
93633510.4%
 
73486710.0%
 
6338279.7%
 
2211226.0%
 
8192005.5%
 
4158734.5%
 
3152754.4%
 
5127993.7%
 
A98702.8%
 
B37561.1%
 
D15220.4%
 
Y13420.4%
 
E10540.3%
 
G7340.2%
 
C6500.2%
 
L5030.1%
 
K3580.1%
 
W3560.1%
 
F2780.1%
 
H164< 0.1%
 
Z80< 0.1%
 
I62< 0.1%
 
P62< 0.1%
 
Other values (4)73< 0.1%
 

REGION
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
I
33022 
L
18606 
D
7487 
A
6812 
P
4002 
ValueCountFrequency (%) 
I3302247.2%
 
L1860626.6%
 
D748710.7%
 
A68129.7%
 
P40025.7%
 
2022-01-09T13:35:15.866600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-01-09T13:35:15.913463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:15.975948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I3302247.2%
 
L1860626.6%
 
D748710.7%
 
A68129.7%
 
P40025.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter69929100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I3302247.2%
 
L1860626.6%
 
D748710.7%
 
A68129.7%
 
P40025.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin69929100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I3302247.2%
 
L1860626.6%
 
D748710.7%
 
A68129.7%
 
P40025.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII69929100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I3302247.2%
 
L1860626.6%
 
D748710.7%
 
A68129.7%
 
P40025.7%
 

CARRIER
Categorical

HIGH CARDINALITY

Distinct267
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
AA
7545 
DL
6757 
UA
6562 
B6
 
1657
WS
 
1342
Other values (262)
46066 
ValueCountFrequency (%) 
AA754510.8%
 
DL67579.7%
 
UA65629.4%
 
B616572.4%
 
WS13421.9%
 
Y411421.6%
 
5X9681.4%
 
AC9471.4%
 
LH9461.4%
 
EV9361.3%
 
KE9241.3%
 
5Y9051.3%
 
OO9021.3%
 
WN8701.2%
 
FX8531.2%
 
09Q7641.1%
 
NK7171.0%
 
AM6781.0%
 
DY6751.0%
 
YX6470.9%
 
VJT6410.9%
 
K86250.9%
 
ABX6130.9%
 
13Q6110.9%
 
CV6100.9%
 
Other values (242)3009243.0%
 
2022-01-09T13:35:16.069676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2022-01-09T13:35:16.147785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.09934362
Min length2

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A2925519.9%
 
L101436.9%
 
D83825.7%
 
U79085.4%
 
Q72404.9%
 
K53033.6%
 
X52553.6%
 
S51443.5%
 
Y47543.2%
 
V45173.1%
 
B43663.0%
 
E42632.9%
 
W38302.6%
 
C37402.5%
 
M33732.3%
 
O31012.1%
 
F28091.9%
 
R25921.8%
 
524441.7%
 
N23931.6%
 
423831.6%
 
623601.6%
 
P21801.5%
 
H21271.4%
 
T19591.3%
 
Other values (11)1498410.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter13096789.2%
 
Decimal Number1583810.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A2925522.3%
 
L101437.7%
 
D83826.4%
 
U79086.0%
 
Q72405.5%
 
K53034.0%
 
X52554.0%
 
S51443.9%
 
Y47543.6%
 
V45173.4%
 
B43663.3%
 
E42633.3%
 
W38302.9%
 
C37402.9%
 
M33732.6%
 
O31012.4%
 
F28092.1%
 
R25922.0%
 
N23931.8%
 
P21801.7%
 
H21271.6%
 
T19591.5%
 
J18501.4%
 
Z17241.3%
 
I16591.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
5244415.4%
 
4238315.0%
 
6236014.9%
 
9170510.8%
 
3161310.2%
 
015199.6%
 
114649.2%
 
89916.3%
 
78965.7%
 
24632.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin13096789.2%
 
Common1583810.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A2925522.3%
 
L101437.7%
 
D83826.4%
 
U79086.0%
 
Q72405.5%
 
K53034.0%
 
X52554.0%
 
S51443.9%
 
Y47543.6%
 
V45173.4%
 
B43663.3%
 
E42633.3%
 
W38302.9%
 
C37402.9%
 
M33732.6%
 
O31012.4%
 
F28092.1%
 
R25922.0%
 
N23931.8%
 
P21801.7%
 
H21271.6%
 
T19591.5%
 
J18501.4%
 
Z17241.3%
 
I16591.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
5244415.4%
 
4238315.0%
 
6236014.9%
 
9170510.8%
 
3161310.2%
 
015199.6%
 
114649.2%
 
89916.3%
 
78965.7%
 
24632.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII146805100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A2925519.9%
 
L101436.9%
 
D83825.7%
 
U79085.4%
 
Q72404.9%
 
K53033.6%
 
X52553.6%
 
S51443.5%
 
Y47543.2%
 
V45173.1%
 
B43663.0%
 
E42632.9%
 
W38302.6%
 
C37402.5%
 
M33732.3%
 
O31012.1%
 
F28091.9%
 
R25921.8%
 
524441.7%
 
N23931.6%
 
423831.6%
 
623601.6%
 
P21801.5%
 
H21271.4%
 
T19591.3%
 
Other values (11)1498410.2%
 

CARRIER_NAME
Categorical

HIGH CARDINALITY

Distinct268
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
American Airlines Inc.
7545 
Delta Air Lines Inc.
6757 
United Air Lines Inc.
6562 
JetBlue Airways
 
1657
Westjet
 
1342
Other values (263)
46066 
ValueCountFrequency (%) 
American Airlines Inc.754510.8%
 
Delta Air Lines Inc.67579.7%
 
United Air Lines Inc.65629.4%
 
JetBlue Airways16572.4%
 
Westjet13421.9%
 
Concesionaria Vuela Compania De Aviacion SA de CV (Volaris)11421.6%
 
United Parcel Service9681.4%
 
Air Canada9471.4%
 
Lufthansa German Airlines9461.4%
 
ExpressJet Airlines Inc.9361.3%
 
Korean Air Lines Co. Ltd.9241.3%
 
Atlas Air Inc.9051.3%
 
SkyWest Airlines Inc.9021.3%
 
Southwest Airlines Co.8701.2%
 
Federal Express Corporation8531.2%
 
Swift Air, LLC7641.1%
 
Spirit Air Lines7171.0%
 
Aeromexico6781.0%
 
Norwegian Air Shuttle ASA6751.0%
 
Republic Airlines6470.9%
 
VistaJet Limited6410.9%
 
Caribbean Sun Airlines, Inc. d/b/a World Atlantic Airlines6250.9%
 
ABX Air Inc6130.9%
 
Chartright Air Inc.6110.9%
 
Cargolux Airlines International S.A6100.9%
 
Other values (243)3009243.0%
 
2022-01-09T13:35:16.257134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2022-01-09T13:35:16.366481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length80
Median length21
Mean length21.4900542
Min length3

Overview of Unicode Properties

Unique unicode characters61
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
16546311.0%
 
i16480111.0%
 
n1313538.7%
 
e1178897.8%
 
r1092507.3%
 
a949756.3%
 
A840875.6%
 
s707294.7%
 
t587123.9%
 
l564083.8%
 
c502423.3%
 
o422562.8%
 
.390682.6%
 
I345032.3%
 
L291671.9%
 
d256781.7%
 
C231191.5%
 
m189331.3%
 
S147821.0%
 
u126640.8%
 
p124460.8%
 
y105190.7%
 
w96050.6%
 
g90220.6%
 
D88730.6%
 
Other values (36)1082347.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter103309268.7%
 
Uppercase Letter25476217.0%
 
Space Separator16546311.0%
 
Other Punctuation448073.0%
 
Open Punctuation18650.1%
 
Close Punctuation18650.1%
 
Dash Punctuation9240.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A8408733.0%
 
I3450313.5%
 
L2916711.4%
 
C231199.1%
 
S147825.8%
 
D88733.5%
 
U84693.3%
 
V56902.2%
 
E51802.0%
 
J50892.0%
 
P50622.0%
 
B48731.9%
 
W36521.4%
 
N33891.3%
 
G29611.2%
 
T28421.1%
 
F24130.9%
 
K19350.8%
 
R18970.7%
 
M18770.7%
 
H16870.7%
 
X9720.4%
 
O9200.4%
 
Q8590.3%
 
Y2830.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i16480116.0%
 
n13135312.7%
 
e11788911.4%
 
r10925010.6%
 
a949759.2%
 
s707296.8%
 
t587125.7%
 
l564085.5%
 
c502424.9%
 
o422564.1%
 
d256782.5%
 
m189331.8%
 
u126641.2%
 
p124461.2%
 
y105191.0%
 
w96050.9%
 
g90220.9%
 
h87420.8%
 
b74440.7%
 
v70760.7%
 
x38440.4%
 
f32630.3%
 
k31500.3%
 
j26460.3%
 
z14440.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
165463100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.3906887.2%
 
/35527.9%
 
,20474.6%
 
&920.2%
 
'480.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-924100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1865100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1865100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin128785485.7%
 
Common21492414.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i16480112.8%
 
n13135310.2%
 
e1178899.2%
 
r1092508.5%
 
a949757.4%
 
A840876.5%
 
s707295.5%
 
t587124.6%
 
l564084.4%
 
c502423.9%
 
o422563.3%
 
I345032.7%
 
L291672.3%
 
d256782.0%
 
C231191.8%
 
m189331.5%
 
S147821.1%
 
u126641.0%
 
p124461.0%
 
y105190.8%
 
w96050.7%
 
g90220.7%
 
D88730.7%
 
h87420.7%
 
U84690.7%
 
Other values (27)806306.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
16546377.0%
 
.3906818.2%
 
/35521.7%
 
,20471.0%
 
(18650.9%
 
)18650.9%
 
-9240.4%
 
&92< 0.1%
 
'48< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1502778100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
16546311.0%
 
i16480111.0%
 
n1313538.7%
 
e1178897.8%
 
r1092507.3%
 
a949756.3%
 
A840875.6%
 
s707294.7%
 
t587123.9%
 
l564083.8%
 
c502423.3%
 
o422562.8%
 
.390682.6%
 
I345032.3%
 
L291671.9%
 
d256781.7%
 
C231191.5%
 
m189331.3%
 
S147821.0%
 
u126640.8%
 
p124460.8%
 
y105190.7%
 
w96050.6%
 
g90220.6%
 
D88730.6%
 
Other values (36)1082347.2%
 

CARRIER_GROUP
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
3
30516 
0
28525 
1
5786 
2
5102 
ValueCountFrequency (%) 
33051643.6%
 
02852540.8%
 
157868.3%
 
251027.3%
 
2022-01-09T13:35:16.460209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-01-09T13:35:16.507072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:16.553936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
33051643.6%
 
02852540.8%
 
157868.3%
 
251027.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number69929100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
33051643.6%
 
02852540.8%
 
157868.3%
 
251027.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common69929100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
33051643.6%
 
02852540.8%
 
157868.3%
 
251027.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII69929100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
33051643.6%
 
02852540.8%
 
157868.3%
 
251027.3%
 

CARRIER_GROUP_NEW
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.664974474
Minimum0
Maximum6
Zeros28525
Zeros (%)40.8%
Memory size546.4 KiB
2022-01-09T13:35:16.616424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.54645776
Coefficient of variation (CV)0.9288176991
Kurtosis-0.8099402765
Mean1.664974474
Median Absolute Deviation (MAD)1
Skewness0.2782118223
Sum116430
Variance2.391531604
MonotocityNot monotonic
2022-01-09T13:35:16.678907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
33051643.6%
 
02852540.8%
 
251027.3%
 
138905.6%
 
614872.1%
 
52300.3%
 
41790.3%
 
ValueCountFrequency (%) 
02852540.8%
 
138905.6%
 
251027.3%
 
33051643.6%
 
41790.3%
 
52300.3%
 
614872.1%
 
ValueCountFrequency (%) 
614872.1%
 
52300.3%
 
41790.3%
 
33051643.6%
 
251027.3%
 
138905.6%
 
02852540.8%
 

ORIGIN_AIRPORT_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct868
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13109.55137
Minimum10135
Maximum16767
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:16.757262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10517
Q111760
median13061
Q314293
95-th percentile16232
Maximum16767
Range6632
Interquartile range (IQR)2533

Descriptive statistics

Standard deviation1664.717776
Coefficient of variation (CV)0.1269851064
Kurtosis-0.7775070518
Mean13109.55137
Median Absolute Deviation (MAD)1248
Skewness0.2424509733
Sum916737818
Variance2771285.272
MonotocityNot monotonic
2022-01-09T13:35:16.850989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1330342216.0%
 
1247829214.2%
 
1289225873.7%
 
1627119912.8%
 
1393019812.8%
 
1226614542.1%
 
1039713872.0%
 
1161813752.0%
 
1129811601.7%
 
1477111431.6%
 
1103211211.6%
 
1169710201.5%
 
107219071.3%
 
132528861.3%
 
162298841.3%
 
122648551.2%
 
132048491.2%
 
129728451.2%
 
162177671.1%
 
122777541.1%
 
121737261.0%
 
137447261.0%
 
117606220.9%
 
147476210.9%
 
118746130.9%
 
Other values (843)3751353.6%
 
ValueCountFrequency (%) 
101353< 0.1%
 
101406< 0.1%
 
1014813< 0.1%
 
10150350.1%
 
101546< 0.1%
 
101589< 0.1%
 
1016220< 0.1%
 
101641< 0.1%
 
101702< 0.1%
 
101711< 0.1%
 
ValueCountFrequency (%) 
167671< 0.1%
 
167193< 0.1%
 
167031< 0.1%
 
167021< 0.1%
 
166551< 0.1%
 
166541< 0.1%
 
166461< 0.1%
 
166194< 0.1%
 
166181< 0.1%
 
166143< 0.1%
 

ORIGIN_AIRPORT_SEQ_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct887
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1310957.844
Minimum1013503
Maximum1676701
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:16.960338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1013503
5-th percentile1051703
Q11176003
median1306104
Q31429302
95-th percentile1623202
Maximum1676701
Range663198
Interquartile range (IQR)253299

Descriptive statistics

Standard deviation166471.6429
Coefficient of variation (CV)0.1269847415
Kurtosis-0.777511402
Mean1310957.844
Median Absolute Deviation (MAD)124798
Skewness0.2424494478
Sum9.167397106e+10
Variance2.771280788e+10
MonotocityNot monotonic
2022-01-09T13:35:17.054065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
133030342216.0%
 
124780329214.2%
 
162710219912.8%
 
139300419812.8%
 
128920417562.5%
 
122660314542.1%
 
103970513872.0%
 
116180213752.0%
 
112980411601.7%
 
147710211431.6%
 
110320111211.6%
 
116970410201.5%
 
10721029071.3%
 
13252028861.3%
 
16229028841.3%
 
12264028551.2%
 
13204028491.2%
 
12972048451.2%
 
12892038311.2%
 
16217027671.1%
 
12277027541.1%
 
13744037261.0%
 
12173027261.0%
 
11760036220.9%
 
14747036210.9%
 
Other values (862)3812654.5%
 
ValueCountFrequency (%) 
10135033< 0.1%
 
10140036< 0.1%
 
101480213< 0.1%
 
1015002350.1%
 
10154036< 0.1%
 
10158049< 0.1%
 
101620220< 0.1%
 
10164031< 0.1%
 
10170012< 0.1%
 
10171021< 0.1%
 
ValueCountFrequency (%) 
16767011< 0.1%
 
16719013< 0.1%
 
16703011< 0.1%
 
16702011< 0.1%
 
16655011< 0.1%
 
16654011< 0.1%
 
16646011< 0.1%
 
16619014< 0.1%
 
16618011< 0.1%
 
16614013< 0.1%
 

ORIGIN_CITY_MARKET_ID
Real number (ℝ≥0)

Distinct755
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32436.83558
Minimum30070
Maximum36742
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:17.153535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30070
5-th percentile30397
Q131032
median32126
Q333252
95-th percentile36106
Maximum36742
Range6672
Interquartile range (IQR)2220

Descriptive statistics

Standard deviation1665.44292
Coefficient of variation (CV)0.05134418604
Kurtosis-0.3238852356
Mean32436.83558
Median Absolute Deviation (MAD)1126
Skewness0.7798506854
Sum2268275475
Variance2773700.121
MonotocityNot monotonic
2022-01-09T13:35:17.247263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3246752797.5%
 
3170346946.7%
 
3257527874.0%
 
3097721413.1%
 
3610620723.0%
 
3145315782.3%
 
3039713992.0%
 
3245713932.0%
 
3085213231.9%
 
3019412251.8%
 
3073011481.6%
 
3103211211.6%
 
307219561.4%
 
332529421.3%
 
312158971.3%
 
314548571.2%
 
321698541.2%
 
360838481.2%
 
305597721.1%
 
319457551.1%
 
321347281.0%
 
309206430.9%
 
317606220.9%
 
318746130.9%
 
343095940.8%
 
Other values (730)3368848.2%
 
ValueCountFrequency (%) 
300702< 0.1%
 
300731< 0.1%
 
301353< 0.1%
 
301406< 0.1%
 
3014813< 0.1%
 
30150350.1%
 
301546< 0.1%
 
301589< 0.1%
 
3016220< 0.1%
 
30164410.1%
 
ValueCountFrequency (%) 
367421< 0.1%
 
366983< 0.1%
 
366841< 0.1%
 
366831< 0.1%
 
366391< 0.1%
 
366381< 0.1%
 
366301< 0.1%
 
366034< 0.1%
 
366021< 0.1%
 
365983< 0.1%
 

ORIGIN
Categorical

HIGH CARDINALITY

Distinct868
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
MIA
 
4221
JFK
 
2921
LAX
 
2587
YYZ
 
1991
ORD
 
1981
Other values (863)
56228 
ValueCountFrequency (%) 
MIA42216.0%
 
JFK29214.2%
 
LAX25873.7%
 
YYZ19912.8%
 
ORD19812.8%
 
IAH14542.1%
 
ATL13872.0%
 
EWR13752.0%
 
DFW11601.7%
 
SFO11431.6%
 
CUN11211.6%
 
FLL10201.5%
 
BOS9071.3%
 
MEX8861.3%
 
YVR8841.3%
 
IAD8551.2%
 
MCO8491.2%
 
LHR8451.2%
 
YUL7671.1%
 
ICN7541.1%
 
HNL7261.0%
 
NRT7261.0%
 
FRA6220.9%
 
SEA6210.9%
 
GDL6130.9%
 
Other values (843)3751353.6%
 
2022-01-09T13:35:17.356614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique226 ?
Unique (%)0.3%
2022-01-09T13:35:17.434718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A196749.4%
 
L149207.1%
 
M128306.1%
 
S118245.6%
 
I105435.0%
 
D102804.9%
 
Y99514.7%
 
R92014.4%
 
C88694.2%
 
F85244.1%
 
O85244.1%
 
N78093.7%
 
P76973.7%
 
G72823.5%
 
E72063.4%
 
H70463.4%
 
T68833.3%
 
U67963.2%
 
J58262.8%
 
X55902.7%
 
K53252.5%
 
B52772.5%
 
W46692.2%
 
V33631.6%
 
Z31831.5%
 
Other values (11)6950.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter209748> 99.9%
 
Decimal Number39< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A196749.4%
 
L149207.1%
 
M128306.1%
 
S118245.6%
 
I105435.0%
 
D102804.9%
 
Y99514.7%
 
R92014.4%
 
C88694.2%
 
F85244.1%
 
O85244.1%
 
N78093.7%
 
P76973.7%
 
G72823.5%
 
E72063.4%
 
H70463.4%
 
T68833.3%
 
U67963.2%
 
J58262.8%
 
X55902.7%
 
K53252.5%
 
B52772.5%
 
W46692.2%
 
V33631.6%
 
Z31831.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1717.9%
 
2615.4%
 
5615.4%
 
6512.8%
 
7410.3%
 
337.7%
 
437.7%
 
925.1%
 
825.1%
 
012.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin209748> 99.9%
 
Common39< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A196749.4%
 
L149207.1%
 
M128306.1%
 
S118245.6%
 
I105435.0%
 
D102804.9%
 
Y99514.7%
 
R92014.4%
 
C88694.2%
 
F85244.1%
 
O85244.1%
 
N78093.7%
 
P76973.7%
 
G72823.5%
 
E72063.4%
 
H70463.4%
 
T68833.3%
 
U67963.2%
 
J58262.8%
 
X55902.7%
 
K53252.5%
 
B52772.5%
 
W46692.2%
 
V33631.6%
 
Z31831.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
1717.9%
 
2615.4%
 
5615.4%
 
6512.8%
 
7410.3%
 
337.7%
 
437.7%
 
925.1%
 
825.1%
 
012.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII209787100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A196749.4%
 
L149207.1%
 
M128306.1%
 
S118245.6%
 
I105435.0%
 
D102804.9%
 
Y99514.7%
 
R92014.4%
 
C88694.2%
 
F85244.1%
 
O85244.1%
 
N78093.7%
 
P76973.7%
 
G72823.5%
 
E72063.4%
 
H70463.4%
 
T68833.3%
 
U67963.2%
 
J58262.8%
 
X55902.7%
 
K53252.5%
 
B52772.5%
 
W46692.2%
 
V33631.6%
 
Z31831.5%
 
Other values (11)6950.3%
 

ORIGIN_CITY_NAME
Categorical

HIGH CARDINALITY

Distinct786
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
Miami, FL
 
4252
New York, NY
 
3230
Los Angeles, CA
 
2587
Chicago, IL
 
2138
Toronto, Canada
 
2072
Other values (781)
55650 
ValueCountFrequency (%) 
Miami, FL42526.1%
 
New York, NY32304.6%
 
Los Angeles, CA25873.7%
 
Chicago, IL21383.1%
 
Toronto, Canada20723.0%
 
Houston, TX15782.3%
 
Atlanta, GA13972.0%
 
Newark, NJ13752.0%
 
Dallas/Fort Worth, TX11661.7%
 
London, United Kingdom11481.6%
 
San Francisco, CA11431.6%
 
Cancun, Mexico11211.6%
 
Washington, DC10501.5%
 
Fort Lauderdale, FL10271.5%
 
Boston, MA9071.3%
 
Vancouver, Canada8971.3%
 
Mexico City, Mexico8861.3%
 
Orlando, FL8571.2%
 
Tokyo, Japan8541.2%
 
Montreal, Canada8481.2%
 
Seoul, South Korea7551.1%
 
Seattle, WA7321.0%
 
Honolulu, HI7281.0%
 
Paris, France6430.9%
 
Frankfurt, Germany6220.9%
 
Other values (761)3591651.4%
 
2022-01-09T13:35:17.528445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique205 ?
Unique (%)0.3%
2022-01-09T13:35:17.622174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length45
Median length15
Mean length15.4310515
Min length8

Overview of Unicode Properties

Unique unicode characters58
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a11701810.8%
 
1011439.4%
 
o711196.6%
 
,700096.5%
 
n697756.5%
 
i581545.4%
 
e574235.3%
 
r421663.9%
 
t388403.6%
 
l343733.2%
 
s279762.6%
 
C258262.4%
 
d255212.4%
 
u251402.3%
 
c215152.0%
 
g185221.7%
 
m182631.7%
 
M180241.7%
 
A173661.6%
 
h169711.6%
 
L168221.6%
 
N141221.3%
 
S126331.2%
 
F122641.1%
 
T116461.1%
 
Other values (33)13644712.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter69738864.6%
 
Uppercase Letter20760619.2%
 
Space Separator1011439.4%
 
Other Punctuation725546.7%
 
Dash Punctuation387< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C2582612.4%
 
M180248.7%
 
A173668.4%
 
L168228.1%
 
N141226.8%
 
S126336.1%
 
F122645.9%
 
T116465.6%
 
P81623.9%
 
Y68833.3%
 
H65683.2%
 
D62763.0%
 
B62273.0%
 
I62053.0%
 
G55112.7%
 
J53662.6%
 
K51752.5%
 
W37491.8%
 
R36751.8%
 
X34061.6%
 
V32211.6%
 
O31921.5%
 
U22171.1%
 
E16020.8%
 
Z9860.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a11701816.8%
 
o7111910.2%
 
n6977510.0%
 
i581548.3%
 
e574238.2%
 
r421666.0%
 
t388405.6%
 
l343734.9%
 
s279764.0%
 
d255213.7%
 
u251403.6%
 
c215153.1%
 
g185222.7%
 
m182632.6%
 
h169712.4%
 
k86401.2%
 
y80391.2%
 
p72261.0%
 
b70521.0%
 
w69121.0%
 
x68991.0%
 
v40290.6%
 
z22290.3%
 
f18500.3%
 
j15490.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
101143100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,7000996.5%
 
/20562.8%
 
.4500.6%
 
'390.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-387100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin90499483.9%
 
Common17408416.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a11701812.9%
 
o711197.9%
 
n697757.7%
 
i581546.4%
 
e574236.3%
 
r421664.7%
 
t388404.3%
 
l343733.8%
 
s279763.1%
 
C258262.9%
 
d255212.8%
 
u251402.8%
 
c215152.4%
 
g185222.0%
 
m182632.0%
 
M180242.0%
 
A173661.9%
 
h169711.9%
 
L168221.9%
 
N141221.6%
 
S126331.4%
 
F122641.4%
 
T116461.3%
 
k86401.0%
 
P81620.9%
 
Other values (27)11671312.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
10114358.1%
 
,7000940.2%
 
/20561.2%
 
.4500.3%
 
-3870.2%
 
'39< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1079078100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a11701810.8%
 
1011439.4%
 
o711196.6%
 
,700096.5%
 
n697756.5%
 
i581545.4%
 
e574235.3%
 
r421663.9%
 
t388403.6%
 
l343733.2%
 
s279762.6%
 
C258262.4%
 
d255212.4%
 
u251402.3%
 
c215152.0%
 
g185221.7%
 
m182631.7%
 
M180241.7%
 
A173661.6%
 
h169711.6%
 
L168221.6%
 
N141221.3%
 
S126331.2%
 
F122641.1%
 
T116461.1%
 
Other values (33)13644712.6%
 

ORIGIN_COUNTRY
Categorical

HIGH CARDINALITY

Distinct135
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size546.4 KiB
US
35777 
CA
6004 
MX
4972 
GB
 
1666
JP
 
1245
Other values (130)
20264 
ValueCountFrequency (%) 
US3577751.2%
 
CA60048.6%
 
MX49727.1%
 
GB16662.4%
 
JP12451.8%
 
DE11501.6%
 
CN11201.6%
 
DO10221.5%
 
BS8481.2%
 
KR7991.1%
 
CO7531.1%
 
FR7171.0%
 
CR6100.9%
 
HK5580.8%
 
BR5560.8%
 
JM5020.7%
 
CU4960.7%
 
IT4720.7%
 
ES4610.7%
 
NL3920.6%
 
HN3760.5%
 
TW3560.5%
 
PA3220.5%
 
IE3150.5%
 
CH3150.5%
 
Other values (110)812411.6%
 
2022-01-09T13:35:17.731525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique12 ?
Unique (%)< 0.1%
2022-01-09T13:35:17.809630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.0000143
Min length2

Overview of Unicode Properties

Unique unicode characters28
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S3824627.3%
 
U3689626.4%
 
C101247.2%
 
A78135.6%
 
M59154.2%
 
X51753.7%
 
B38352.7%
 
E32922.4%
 
R32492.3%
 
G26781.9%
 
N25591.8%
 
D24121.7%
 
P23061.6%
 
T22441.6%
 
O19741.4%
 
K19401.4%
 
J18161.3%
 
H16741.2%
 
I14921.1%
 
L10050.7%
 
F9380.7%
 
W7190.5%
 
V6190.4%
 
Y3620.3%
 
Z3300.2%
 
Other values (3)2460.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter139856> 99.9%
 
Lowercase Letter3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S3824627.3%
 
U3689626.4%
 
C101247.2%
 
A78135.6%
 
M59154.2%
 
X51753.7%
 
B38352.7%
 
E32922.4%
 
R32492.3%
 
G26781.9%
 
N25591.8%
 
D24121.7%
 
P23061.6%
 
T22441.6%
 
O19741.4%
 
K19401.4%
 
J18161.3%
 
H16741.2%
 
I14921.1%
 
L10050.7%
 
F9380.7%
 
W7190.5%
 
V6190.4%
 
Y3620.3%
 
Z3300.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n266.7%
 
a133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin139859100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S3824627.3%
 
U3689626.4%
 
C101247.2%
 
A78135.6%
 
M59154.2%
 
X51753.7%
 
B38352.7%
 
E32922.4%
 
R32492.3%
 
G26781.9%
 
N25591.8%
 
D24121.7%
 
P23061.6%
 
T22441.6%
 
O19741.4%
 
K19401.4%
 
J18161.3%
 
H16741.2%
 
I14921.1%
 
L10050.7%
 
F9380.7%
 
W7190.5%
 
V6190.4%
 
Y3620.3%
 
Z3300.2%
 
Other values (3)2460.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII139859100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S3824627.3%
 
U3689626.4%
 
C101247.2%
 
A78135.6%
 
M59154.2%
 
X51753.7%
 
B38352.7%
 
E32922.4%
 
R32492.3%
 
G26781.9%
 
N25591.8%
 
D24121.7%
 
P23061.6%
 
T22441.6%
 
O19741.4%
 
K19401.4%
 
J18161.3%
 
H16741.2%
 
I14921.1%
 
L10050.7%
 
F9380.7%
 
W7190.5%
 
V6190.4%
 
Y3620.3%
 
Z3300.2%
 
Other values (3)2460.2%
 

ORIGIN_COUNTRY_NAME
Categorical

HIGH CARDINALITY

Distinct136
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
United States
35777 
Canada
6004 
Mexico
4972 
United Kingdom
 
1666
Japan
 
1245
Other values (131)
20265 
ValueCountFrequency (%) 
United States3577751.2%
 
Canada60048.6%
 
Mexico49727.1%
 
United Kingdom16662.4%
 
Japan12451.8%
 
Germany11501.6%
 
China11201.6%
 
Dominican Republic10221.5%
 
The Bahamas8481.2%
 
South Korea7991.1%
 
Colombia7531.1%
 
France7171.0%
 
Costa Rica6100.9%
 
Hong Kong5580.8%
 
Brazil5560.8%
 
Jamaica5020.7%
 
Cuba4960.7%
 
Italy4720.7%
 
Spain4610.7%
 
Netherlands3920.6%
 
Honduras3760.5%
 
Taiwan3560.5%
 
Panama3220.5%
 
Ireland3150.5%
 
Switzerland3150.5%
 
Other values (111)812511.6%
 
2022-01-09T13:35:17.918983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13 ?
Unique (%)< 0.1%
2022-01-09T13:35:18.012712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length13
Mean length10.62248852
Min length4

Overview of Unicode Properties

Unique unicode characters50
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t11516715.5%
 
e8996812.1%
 
a8406711.3%
 
n602028.1%
 
i576507.8%
 
d503716.8%
 
451456.1%
 
s415005.6%
 
S387845.2%
 
U377325.1%
 
o153482.1%
 
c101431.4%
 
r100511.4%
 
C96861.3%
 
m78451.1%
 
l75761.0%
 
u67900.9%
 
M54520.7%
 
x51110.7%
 
h46830.6%
 
g42270.6%
 
b34860.5%
 
p32080.4%
 
K31770.4%
 
y23240.3%
 
Other values (25)231273.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter58326278.5%
 
Uppercase Letter11433315.4%
 
Space Separator451456.1%
 
Other Punctuation80< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S3878433.9%
 
U3773233.0%
 
C96868.5%
 
M54524.8%
 
K31772.8%
 
B22592.0%
 
T19031.7%
 
I18221.6%
 
R18201.6%
 
G17951.6%
 
J17881.6%
 
A13271.2%
 
D11991.0%
 
H11231.0%
 
P9860.9%
 
F9360.8%
 
E9100.8%
 
N8830.8%
 
V2940.3%
 
L2370.2%
 
Q1360.1%
 
Z840.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t11516719.7%
 
e8996815.4%
 
a8406714.4%
 
n6020210.3%
 
i576509.9%
 
d503718.6%
 
s415007.1%
 
o153482.6%
 
c101431.7%
 
r100511.7%
 
m78451.3%
 
l75761.3%
 
u67901.2%
 
x51110.9%
 
h46830.8%
 
g42270.7%
 
b34860.6%
 
p32080.6%
 
y23240.4%
 
z12550.2%
 
w9980.2%
 
k5440.1%
 
v4690.1%
 
f156< 0.1%
 
q67< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
45145100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,80100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin69759593.9%
 
Common452256.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t11516716.5%
 
e8996812.9%
 
a8406712.1%
 
n602028.6%
 
i576508.3%
 
d503717.2%
 
s415005.9%
 
S387845.6%
 
U377325.4%
 
o153482.2%
 
c101431.5%
 
r100511.4%
 
C96861.4%
 
m78451.1%
 
l75761.1%
 
u67901.0%
 
M54520.8%
 
x51110.7%
 
h46830.7%
 
g42270.6%
 
b34860.5%
 
p32080.5%
 
K31770.5%
 
y23240.3%
 
B22590.3%
 
Other values (23)207883.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
4514599.8%
 
,800.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII742820100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t11516715.5%
 
e8996812.1%
 
a8406711.3%
 
n602028.1%
 
i576507.8%
 
d503716.8%
 
451456.1%
 
s415005.6%
 
S387845.2%
 
U377325.1%
 
o153482.1%
 
c101431.4%
 
r100511.4%
 
C96861.3%
 
m78451.1%
 
l75761.0%
 
u67900.9%
 
M54520.7%
 
x51110.7%
 
h46830.6%
 
g42270.6%
 
b34860.5%
 
p32080.4%
 
K31770.4%
 
y23240.3%
 
Other values (25)231273.1%
 

ORIGIN_WAC
Real number (ℝ≥0)

Distinct200
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259.2269016
Minimum1
Maximum975
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:18.090814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q134
median93
Q3429
95-th percentile936
Maximum975
Range974
Interquartile range (IQR)395

Descriptive statistics

Standard deviation297.2641996
Coefficient of variation (CV)1.146733606
Kurtosis0.02090650404
Mean259.2269016
Median Absolute Deviation (MAD)71
Skewness1.192728142
Sum18127478
Variance88366.00436
MonotocityNot monotonic
2022-01-09T13:35:18.184540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3368169.7%
 
14849727.1%
 
9145436.5%
 
2234655.0%
 
7434004.9%
 
93625963.7%
 
4121493.1%
 
2116732.4%
 
49316662.4%
 
3414182.0%
 
73612451.8%
 
42911501.6%
 
3811441.6%
 
90611341.6%
 
71311201.6%
 
22410221.5%
 
139441.3%
 
9419411.3%
 
2048481.2%
 
938351.2%
 
9168191.2%
 
28051.2%
 
7787991.1%
 
3277531.1%
 
237261.0%
 
Other values (175)2294632.8%
 
ValueCountFrequency (%) 
13910.6%
 
28051.2%
 
36210.9%
 
4540.1%
 
55740.8%
 
11860.1%
 
1218< 0.1%
 
139441.3%
 
1411< 0.1%
 
15480.1%
 
ValueCountFrequency (%) 
9751< 0.1%
 
961470.1%
 
9566< 0.1%
 
9511250.2%
 
94628< 0.1%
 
9419411.3%
 
93625963.7%
 
9313< 0.1%
 
9262390.3%
 
921650.1%
 

DEST_AIRPORT_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct876
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13102.05476
Minimum10128
Maximum16751
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:18.293890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10128
5-th percentile10411
Q111731
median13043
Q314307
95-th percentile16232
Maximum16751
Range6623
Interquartile range (IQR)2576

Descriptive statistics

Standard deviation1687.527454
Coefficient of variation (CV)0.1287986874
Kurtosis-0.8220488478
Mean13102.05476
Median Absolute Deviation (MAD)1269
Skewness0.2216572534
Sum916213587
Variance2847748.909
MonotocityNot monotonic
2022-01-09T13:35:18.387617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1330337645.4%
 
1247828934.1%
 
1289224553.5%
 
1627119732.8%
 
1393018452.6%
 
1161813862.0%
 
1039713351.9%
 
1226613181.9%
 
1129811061.6%
 
1477110991.6%
 
1103210401.5%
 
1169710291.5%
 
107219191.3%
 
129728711.2%
 
122648681.2%
 
162298591.2%
 
132528491.2%
 
132048241.2%
 
122777991.1%
 
137447651.1%
 
162177571.1%
 
117606480.9%
 
109206440.9%
 
118746430.9%
 
121736140.9%
 
Other values (851)3862655.2%
 
ValueCountFrequency (%) 
101281< 0.1%
 
101353< 0.1%
 
101361< 0.1%
 
101406< 0.1%
 
101441< 0.1%
 
1014813< 0.1%
 
10150450.1%
 
101543< 0.1%
 
101551< 0.1%
 
101587< 0.1%
 
ValueCountFrequency (%) 
167511< 0.1%
 
167321< 0.1%
 
167193< 0.1%
 
167021< 0.1%
 
166461< 0.1%
 
1659911< 0.1%
 
165751< 0.1%
 
165261< 0.1%
 
165231< 0.1%
 
164844< 0.1%
 

DEST_AIRPORT_SEQ_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct900
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1310208.175
Minimum1012803
Maximum1675101
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:18.481318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1012803
5-th percentile1041103
Q11173103
median1304305
Q31430702
95-th percentile1623202
Maximum1675101
Range662298
Interquartile range (IQR)257599

Descriptive statistics

Standard deviation168752.6202
Coefficient of variation (CV)0.1287983264
Kurtosis-0.8220527122
Mean1310208.175
Median Absolute Deviation (MAD)126897
Skewness0.2216556361
Sum9.162154748e+10
Variance2.847744682e+10
MonotocityNot monotonic
2022-01-09T13:35:18.590667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
133030337645.4%
 
124780328934.1%
 
162710219732.8%
 
139300418452.6%
 
128920416602.4%
 
116180213862.0%
 
103970513351.9%
 
122660313181.9%
 
112980411061.6%
 
147710210991.6%
 
110320110401.5%
 
116970410291.5%
 
10721029191.3%
 
12972048711.2%
 
12264028681.2%
 
16229028591.2%
 
13252028491.2%
 
13204028241.2%
 
12277027991.1%
 
12892037951.1%
 
13744037651.1%
 
16217027571.1%
 
11760036480.9%
 
10920036440.9%
 
11874046430.9%
 
Other values (875)3924056.1%
 
ValueCountFrequency (%) 
10128031< 0.1%
 
10135033< 0.1%
 
10136031< 0.1%
 
10140036< 0.1%
 
10144021< 0.1%
 
101480213< 0.1%
 
1015002450.1%
 
10154033< 0.1%
 
10155021< 0.1%
 
10158047< 0.1%
 
ValueCountFrequency (%) 
16751011< 0.1%
 
16732011< 0.1%
 
16719013< 0.1%
 
16702011< 0.1%
 
16646011< 0.1%
 
165990111< 0.1%
 
16575011< 0.1%
 
16526021< 0.1%
 
16523011< 0.1%
 
16484014< 0.1%
 

DEST_CITY_MARKET_ID
Real number (ℝ≥0)

Distinct759
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32444.43863
Minimum30082
Maximum36727
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:18.700520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30082
5-th percentile30325
Q131032
median32105
Q333342
95-th percentile36106
Maximum36727
Range6645
Interquartile range (IQR)2310

Descriptive statistics

Standard deviation1683.946484
Coefficient of variation (CV)0.05190246942
Kurtosis-0.398038122
Mean32444.43863
Median Absolute Deviation (MAD)1128
Skewness0.7567184243
Sum2268807149
Variance2835675.76
MonotocityNot monotonic
2022-01-09T13:35:18.799764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3246748356.9%
 
3170346526.7%
 
3257526063.7%
 
3610620552.9%
 
3097720092.9%
 
3145314492.1%
 
3245713571.9%
 
3039713451.9%
 
3085212721.8%
 
3073011881.7%
 
3019411281.6%
 
3103210401.5%
 
307219561.4%
 
332529041.3%
 
321699031.3%
 
312158721.2%
 
314548301.2%
 
360838231.2%
 
319458051.2%
 
305597741.1%
 
309207321.0%
 
317606480.9%
 
318746430.9%
 
321346210.9%
 
302926030.9%
 
Other values (734)3487949.9%
 
ValueCountFrequency (%) 
300821< 0.1%
 
301281< 0.1%
 
301353< 0.1%
 
301361< 0.1%
 
301406< 0.1%
 
301441< 0.1%
 
3014813< 0.1%
 
30150450.1%
 
301543< 0.1%
 
301551< 0.1%
 
ValueCountFrequency (%) 
367271< 0.1%
 
367111< 0.1%
 
366983< 0.1%
 
366831< 0.1%
 
366301< 0.1%
 
3658311< 0.1%
 
365611< 0.1%
 
365161< 0.1%
 
365131< 0.1%
 
364744< 0.1%
 

DEST
Categorical

HIGH CARDINALITY

Distinct876
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
MIA
 
3764
JFK
 
2893
LAX
 
2455
YYZ
 
1973
ORD
 
1845
Other values (871)
56999 
ValueCountFrequency (%) 
MIA37645.4%
 
JFK28934.1%
 
LAX24553.5%
 
YYZ19732.8%
 
ORD18452.6%
 
EWR13862.0%
 
ATL13351.9%
 
IAH13181.9%
 
DFW11061.6%
 
SFO10991.6%
 
CUN10401.5%
 
FLL10291.5%
 
BOS9191.3%
 
LHR8711.2%
 
IAD8681.2%
 
YVR8591.2%
 
MEX8491.2%
 
MCO8241.2%
 
ICN7991.1%
 
NRT7651.1%
 
YUL7571.1%
 
FRA6480.9%
 
CDG6440.9%
 
GDL6430.9%
 
HNL6140.9%
 
Other values (851)3862655.2%
 
2022-01-09T13:35:18.924762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique217 ?
Unique (%)0.3%
2022-01-09T13:35:19.002868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A195589.3%
 
L146137.0%
 
M127466.1%
 
S124455.9%
 
D103434.9%
 
I101904.9%
 
Y100804.8%
 
R91444.4%
 
C89824.3%
 
F85084.1%
 
O84114.0%
 
N79283.8%
 
P76003.6%
 
E72123.4%
 
G71633.4%
 
T69693.3%
 
H68633.3%
 
U68363.3%
 
J59832.9%
 
X55362.6%
 
B54712.6%
 
K53092.5%
 
W46932.2%
 
Z32791.6%
 
V32691.6%
 
Other values (6)6560.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter209779> 99.9%
 
Decimal Number8< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A195589.3%
 
L146137.0%
 
M127466.1%
 
S124455.9%
 
D103434.9%
 
I101904.9%
 
Y100804.8%
 
R91444.4%
 
C89824.3%
 
F85084.1%
 
O84114.0%
 
N79283.8%
 
P76003.6%
 
E72123.4%
 
G71633.4%
 
T69693.3%
 
H68633.3%
 
U68363.3%
 
J59832.9%
 
X55362.6%
 
B54712.6%
 
K53092.5%
 
W46932.2%
 
Z32791.6%
 
V32691.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2337.5%
 
8225.0%
 
3112.5%
 
4112.5%
 
7112.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin209779> 99.9%
 
Common8< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A195589.3%
 
L146137.0%
 
M127466.1%
 
S124455.9%
 
D103434.9%
 
I101904.9%
 
Y100804.8%
 
R91444.4%
 
C89824.3%
 
F85084.1%
 
O84114.0%
 
N79283.8%
 
P76003.6%
 
E72123.4%
 
G71633.4%
 
T69693.3%
 
H68633.3%
 
U68363.3%
 
J59832.9%
 
X55362.6%
 
B54712.6%
 
K53092.5%
 
W46932.2%
 
Z32791.6%
 
V32691.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
2337.5%
 
8225.0%
 
3112.5%
 
4112.5%
 
7112.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII209787100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A195589.3%
 
L146137.0%
 
M127466.1%
 
S124455.9%
 
D103434.9%
 
I101904.9%
 
Y100804.8%
 
R91444.4%
 
C89824.3%
 
F85084.1%
 
O84114.0%
 
N79283.8%
 
P76003.6%
 
E72123.4%
 
G71633.4%
 
T69693.3%
 
H68633.3%
 
U68363.3%
 
J59832.9%
 
X55362.6%
 
B54712.6%
 
K53092.5%
 
W46932.2%
 
Z32791.6%
 
V32691.6%
 
Other values (6)6560.3%
 

DEST_CITY_NAME
Categorical

HIGH CARDINALITY

Distinct790
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
Miami, FL
 
3800
New York, NY
 
3169
Los Angeles, CA
 
2455
Toronto, Canada
 
2055
Chicago, IL
 
2000
Other values (785)
56450 
ValueCountFrequency (%) 
Miami, FL38005.4%
 
New York, NY31694.5%
 
Los Angeles, CA24553.5%
 
Toronto, Canada20552.9%
 
Chicago, IL20002.9%
 
Houston, TX14492.1%
 
Newark, NJ13862.0%
 
Atlanta, GA13441.9%
 
London, United Kingdom11881.7%
 
Dallas/Fort Worth, TX11111.6%
 
San Francisco, CA10991.6%
 
Cancun, Mexico10401.5%
 
Fort Lauderdale, FL10351.5%
 
Washington, DC10121.4%
 
Boston, MA9191.3%
 
Tokyo, Japan9031.3%
 
Vancouver, Canada8721.2%
 
Mexico City, Mexico8491.2%
 
Orlando, FL8301.2%
 
Montreal, Canada8231.2%
 
Seoul, South Korea8051.2%
 
Paris, France7321.0%
 
Seattle, WA7281.0%
 
Frankfurt, Germany6480.9%
 
Guadalajara, Mexico6430.9%
 
Other values (765)3703453.0%
 
2022-01-09T13:35:19.096596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique195 ?
Unique (%)0.3%
2022-01-09T13:35:19.205953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length42
Median length15
Mean length15.65224728
Min length8

Overview of Unicode Properties

Unique unicode characters60
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a12018911.0%
 
1017129.3%
 
o709346.5%
 
n704596.4%
 
,700036.4%
 
e594985.4%
 
i589215.4%
 
r442024.0%
 
t399053.6%
 
l353523.2%
 
s287592.6%
 
d271012.5%
 
u258162.4%
 
C254222.3%
 
c215732.0%
 
m190531.7%
 
g181321.7%
 
M177331.6%
 
A176361.6%
 
h170021.6%
 
L161551.5%
 
N141901.3%
 
S129601.2%
 
F119771.1%
 
T113261.0%
 
Other values (35)13853612.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter71337865.2%
 
Uppercase Letter20643618.9%
 
Space Separator1017129.3%
 
Other Punctuation726056.6%
 
Dash Punctuation413< 0.1%
 
Open Punctuation1< 0.1%
 
Close Punctuation1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C2542212.3%
 
M177338.6%
 
A176368.5%
 
L161557.8%
 
N141906.9%
 
S129606.3%
 
F119775.8%
 
T113265.5%
 
P84414.1%
 
B68013.3%
 
Y67903.3%
 
D61663.0%
 
I61263.0%
 
H60042.9%
 
G57802.8%
 
J55502.7%
 
K51212.5%
 
R37071.8%
 
W36251.8%
 
V31481.5%
 
X31231.5%
 
O30991.5%
 
U23831.2%
 
E17160.8%
 
Z10000.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a12018916.8%
 
o709349.9%
 
n704599.9%
 
e594988.3%
 
i589218.3%
 
r442026.2%
 
t399055.6%
 
l353525.0%
 
s287594.0%
 
d271013.8%
 
u258163.6%
 
c215733.0%
 
m190532.7%
 
g181322.5%
 
h170022.4%
 
k86881.2%
 
y86041.2%
 
b74031.0%
 
p72991.0%
 
x70271.0%
 
w70151.0%
 
v40480.6%
 
z26440.4%
 
f18850.3%
 
j16720.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,7000396.4%
 
/20872.9%
 
.4740.7%
 
'410.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
101712100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-413100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin91981484.0%
 
Common17473216.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a12018913.1%
 
o709347.7%
 
n704597.7%
 
e594986.5%
 
i589216.4%
 
r442024.8%
 
t399054.3%
 
l353523.8%
 
s287593.1%
 
d271012.9%
 
u258162.8%
 
C254222.8%
 
c215732.3%
 
m190532.1%
 
g181322.0%
 
M177331.9%
 
A176361.9%
 
h170021.8%
 
L161551.8%
 
N141901.5%
 
S129601.4%
 
F119771.3%
 
T113261.2%
 
k86880.9%
 
y86040.9%
 
Other values (27)11822712.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
10171258.2%
 
,7000340.1%
 
/20871.2%
 
.4740.3%
 
-4130.2%
 
'41< 0.1%
 
(1< 0.1%
 
)1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1094546100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a12018911.0%
 
1017129.3%
 
o709346.5%
 
n704596.4%
 
,700036.4%
 
e594985.4%
 
i589215.4%
 
r442024.0%
 
t399053.6%
 
l353523.2%
 
s287592.6%
 
d271012.5%
 
u258162.4%
 
C254222.3%
 
c215732.0%
 
m190531.7%
 
g181321.7%
 
M177331.6%
 
A176361.6%
 
h170021.6%
 
L161551.5%
 
N141901.3%
 
S129601.2%
 
F119771.1%
 
T113261.0%
 
Other values (35)13853612.7%
 

DEST_COUNTRY
Categorical

HIGH CARDINALITY

Distinct146
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
US
34152 
CA
5991 
MX
5002 
GB
 
1770
JP
 
1286
Other values (141)
21728 
ValueCountFrequency (%) 
US3415248.8%
 
CA59918.6%
 
MX50027.2%
 
GB17702.5%
 
JP12861.8%
 
DE12361.8%
 
CN10341.5%
 
DO10201.5%
 
BS8871.3%
 
KR8551.2%
 
FR8521.2%
 
BR7801.1%
 
CO7341.0%
 
CR6260.9%
 
NL6040.9%
 
JM5470.8%
 
CU5180.7%
 
ES4660.7%
 
IT4600.7%
 
HK4170.6%
 
GT3910.6%
 
AU3810.5%
 
TW3590.5%
 
PA3580.5%
 
HN3540.5%
 
Other values (121)884912.7%
 
2022-01-09T13:35:19.299673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13 ?
Unique (%)< 0.1%
2022-01-09T13:35:19.393401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S3671826.3%
 
U3546425.4%
 
C101117.2%
 
A80785.8%
 
M59964.3%
 
X52403.7%
 
B43183.1%
 
R36632.6%
 
E34402.5%
 
G29222.1%
 
N27762.0%
 
D24841.8%
 
P24161.7%
 
T23541.7%
 
O19821.4%
 
J19111.4%
 
K18971.4%
 
H15701.1%
 
I15301.1%
 
L12870.9%
 
F10880.8%
 
W7640.5%
 
V6800.5%
 
Y5130.4%
 
Z4070.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter139858100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S3671826.3%
 
U3546425.4%
 
C101117.2%
 
A80785.8%
 
M59964.3%
 
X52403.7%
 
B43183.1%
 
R36632.6%
 
E34402.5%
 
G29222.1%
 
N27762.0%
 
D24841.8%
 
P24161.7%
 
T23541.7%
 
O19821.4%
 
J19111.4%
 
K18971.4%
 
H15701.1%
 
I15301.1%
 
L12870.9%
 
F10880.8%
 
W7640.5%
 
V6800.5%
 
Y5130.4%
 
Z4070.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin139858100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S3671826.3%
 
U3546425.4%
 
C101117.2%
 
A80785.8%
 
M59964.3%
 
X52403.7%
 
B43183.1%
 
R36632.6%
 
E34402.5%
 
G29222.1%
 
N27762.0%
 
D24841.8%
 
P24161.7%
 
T23541.7%
 
O19821.4%
 
J19111.4%
 
K18971.4%
 
H15701.1%
 
I15301.1%
 
L12870.9%
 
F10880.8%
 
W7640.5%
 
V6800.5%
 
Y5130.4%
 
Z4070.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII139858100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S3671826.3%
 
U3546425.4%
 
C101117.2%
 
A80785.8%
 
M59964.3%
 
X52403.7%
 
B43183.1%
 
R36632.6%
 
E34402.5%
 
G29222.1%
 
N27762.0%
 
D24841.8%
 
P24161.7%
 
T23541.7%
 
O19821.4%
 
J19111.4%
 
K18971.4%
 
H15701.1%
 
I15301.1%
 
L12870.9%
 
F10880.8%
 
W7640.5%
 
V6800.5%
 
Y5130.4%
 
Z4070.3%
 

DEST_COUNTRY_NAME
Categorical

HIGH CARDINALITY

Distinct146
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
United States
34152 
Canada
5991 
Mexico
5002 
United Kingdom
 
1770
Japan
 
1286
Other values (141)
21728 
ValueCountFrequency (%) 
United States3415248.8%
 
Canada59918.6%
 
Mexico50027.2%
 
United Kingdom17702.5%
 
Japan12861.8%
 
Germany12361.8%
 
China10341.5%
 
Dominican Republic10201.5%
 
The Bahamas8871.3%
 
South Korea8551.2%
 
France8521.2%
 
Brazil7801.1%
 
Colombia7341.0%
 
Costa Rica6260.9%
 
Netherlands6040.9%
 
Jamaica5470.8%
 
Cuba5180.7%
 
Spain4660.7%
 
Italy4600.7%
 
Hong Kong4170.6%
 
Guatemala3910.6%
 
Australia3810.5%
 
Taiwan3590.5%
 
Panama3580.5%
 
Honduras3540.5%
 
Other values (121)884912.7%
 
2022-01-09T13:35:19.487128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13 ?
Unique (%)< 0.1%
2022-01-09T13:35:19.581154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length13
Mean length10.54974331
Min length4

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t11110515.1%
 
e8813011.9%
 
a8509311.5%
 
n594848.1%
 
i571047.7%
 
d495016.7%
 
440356.0%
 
s405485.5%
 
S372765.1%
 
U363104.9%
 
o154732.1%
 
r113941.5%
 
c103991.4%
 
C97081.3%
 
l84571.1%
 
m83631.1%
 
u73091.0%
 
M55200.7%
 
x51960.7%
 
h50350.7%
 
g43360.6%
 
b36660.5%
 
p32920.4%
 
K32000.4%
 
B26510.4%
 
Other values (27)251483.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter58043578.7%
 
Uppercase Letter11318715.3%
 
Space Separator440356.0%
 
Other Punctuation74< 0.1%
 
Open Punctuation1< 0.1%
 
Close Punctuation1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S3727632.9%
 
U3631032.1%
 
C97088.6%
 
M55204.9%
 
K32002.8%
 
B26512.3%
 
T19901.8%
 
I19771.7%
 
G19541.7%
 
J18781.7%
 
R18061.6%
 
A15541.4%
 
D11961.1%
 
N11711.0%
 
F10831.0%
 
P10570.9%
 
E9860.9%
 
H9610.8%
 
V3330.3%
 
L2960.3%
 
Q1410.1%
 
Z1390.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t11110519.1%
 
e8813015.2%
 
a8509314.7%
 
n5948410.2%
 
i571049.8%
 
d495018.5%
 
s405487.0%
 
o154732.7%
 
r113942.0%
 
c103991.8%
 
l84571.5%
 
m83631.4%
 
u73091.3%
 
x51960.9%
 
h50350.9%
 
g43360.7%
 
b36660.6%
 
p32920.6%
 
y25710.4%
 
z15340.3%
 
w10790.2%
 
k5720.1%
 
v5000.1%
 
f162< 0.1%
 
q71< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
44035100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,74100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin69362294.0%
 
Common441116.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t11110516.0%
 
e8813012.7%
 
a8509312.3%
 
n594848.6%
 
i571048.2%
 
d495017.1%
 
s405485.8%
 
S372765.4%
 
U363105.2%
 
o154732.2%
 
r113941.6%
 
c103991.5%
 
C97081.4%
 
l84571.2%
 
m83631.2%
 
u73091.1%
 
M55200.8%
 
x51960.7%
 
h50350.7%
 
g43360.6%
 
b36660.5%
 
p32920.5%
 
K32000.5%
 
B26510.4%
 
y25710.4%
 
Other values (23)225013.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
4403599.8%
 
,740.2%
 
(1< 0.1%
 
)1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII737733100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t11110515.1%
 
e8813011.9%
 
a8509311.5%
 
n594848.1%
 
i571047.7%
 
d495016.7%
 
440356.0%
 
s405485.5%
 
S372765.1%
 
U363104.9%
 
o154732.1%
 
r113941.5%
 
c103991.4%
 
C97081.3%
 
l84571.1%
 
m83631.1%
 
u73091.0%
 
M55200.7%
 
x51960.7%
 
h50350.7%
 
g43360.6%
 
b36660.5%
 
p32920.4%
 
K32000.4%
 
B26510.4%
 
Other values (27)251483.4%
 

DEST_WAC
Real number (ℝ≥0)

Distinct209
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.22111
Minimum1
Maximum961
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:19.674882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q136
median118
Q3433
95-th percentile936
Maximum961
Range960
Interquartile range (IQR)397

Descriptive statistics

Standard deviation297.4513637
Coefficient of variation (CV)1.113128239
Kurtosis-0.08767486721
Mean267.22111
Median Absolute Deviation (MAD)96
Skewness1.136444338
Sum18686505
Variance88477.31374
MonotocityNot monotonic
2022-01-09T13:35:19.768609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3363809.1%
 
14850027.2%
 
9143256.2%
 
2233974.9%
 
7431174.5%
 
93626933.9%
 
4120132.9%
 
49317702.5%
 
2116762.4%
 
3413702.0%
 
73612861.8%
 
42912361.8%
 
90610941.6%
 
3810671.5%
 
71310341.5%
 
22410201.5%
 
139481.4%
 
9419041.3%
 
2048871.3%
 
7788551.2%
 
4278521.2%
 
9168171.2%
 
938141.2%
 
3167801.1%
 
3277341.0%
 
Other values (184)2385834.1%
 
ValueCountFrequency (%) 
14630.7%
 
26971.0%
 
36170.9%
 
4460.1%
 
55210.7%
 
11790.1%
 
12430.1%
 
139481.4%
 
1414< 0.1%
 
1534< 0.1%
 
ValueCountFrequency (%) 
961540.1%
 
9566< 0.1%
 
9511170.2%
 
94625< 0.1%
 
9419041.3%
 
93626933.9%
 
9262190.3%
 
921610.1%
 
9168171.2%
 
9111< 0.1%
 

YEAR
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
2016
69929 
ValueCountFrequency (%) 
201669929100.0%
 
2022-01-09T13:35:19.846716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-01-09T13:35:19.893579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:19.940445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
26992925.0%
 
06992925.0%
 
16992925.0%
 
66992925.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number279716100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
26992925.0%
 
06992925.0%
 
16992925.0%
 
66992925.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common279716100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
26992925.0%
 
06992925.0%
 
16992925.0%
 
66992925.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII279716100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
26992925.0%
 
06992925.0%
 
16992925.0%
 
66992925.0%
 

QUARTER
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
3
17581 
2
17522 
4
17433 
1
17393 
ValueCountFrequency (%) 
31758125.1%
 
21752225.1%
 
41743324.9%
 
11739324.9%
 
2022-01-09T13:35:20.018554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-01-09T13:35:20.065416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:20.112278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
31758125.1%
 
21752225.1%
 
41743324.9%
 
11739324.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number69929100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
31758125.1%
 
21752225.1%
 
41743324.9%
 
11739324.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common69929100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
31758125.1%
 
21752225.1%
 
41743324.9%
 
11739324.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII69929100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
31758125.1%
 
21752225.1%
 
41743324.9%
 
11739324.9%
 

MONTH
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.49997855
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:20.190384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.450949562
Coefficient of variation (CV)0.5309170694
Kurtosis-1.207608921
Mean6.49997855
Median Absolute Deviation (MAD)3
Skewness0.004439513632
Sum454537
Variance11.90905288
MonotocityNot monotonic
2022-01-09T13:35:20.253096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
860208.6%
 
1259828.6%
 
759738.5%
 
459078.4%
 
158858.4%
 
358468.4%
 
558158.3%
 
658008.3%
 
1157308.2%
 
1057218.2%
 
256628.1%
 
955888.0%
 
ValueCountFrequency (%) 
158858.4%
 
256628.1%
 
358468.4%
 
459078.4%
 
558158.3%
 
658008.3%
 
759738.5%
 
860208.6%
 
955888.0%
 
1057218.2%
 
ValueCountFrequency (%) 
1259828.6%
 
1157308.2%
 
1057218.2%
 
955888.0%
 
860208.6%
 
759738.5%
 
658008.3%
 
558158.3%
 
459078.4%
 
358468.4%
 

DISTANCE_GROUP
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.224770839
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Memory size546.4 KiB
2022-01-09T13:35:20.315583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q39
95-th percentile15
Maximum22
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.475976392
Coefficient of variation (CV)0.7190588228
Kurtosis-0.3581810983
Mean6.224770839
Median Absolute Deviation (MAD)3
Skewness0.793858927
Sum435292
Variance20.03436466
MonotocityNot monotonic
2022-01-09T13:35:20.393687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
31185517.0%
 
4902612.9%
 
1755610.8%
 
2745310.7%
 
946146.6%
 
844936.4%
 
540935.9%
 
1135295.0%
 
1035005.0%
 
1223643.4%
 
1422323.2%
 
720622.9%
 
1517112.4%
 
616132.3%
 
1313531.9%
 
1710001.4%
 
168461.2%
 
182730.4%
 
191730.2%
 
201560.2%
 
2126< 0.1%
 
221< 0.1%
 
ValueCountFrequency (%) 
1755610.8%
 
2745310.7%
 
31185517.0%
 
4902612.9%
 
540935.9%
 
616132.3%
 
720622.9%
 
844936.4%
 
946146.6%
 
1035005.0%
 
ValueCountFrequency (%) 
221< 0.1%
 
2126< 0.1%
 
201560.2%
 
191730.2%
 
182730.4%
 
1710001.4%
 
168461.2%
 
1517112.4%
 
1422323.2%
 
1313531.9%
 

CLASS
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size546.4 KiB
F
50531 
G
7883 
L
7854 
P
 
3661
ValueCountFrequency (%) 
F5053172.3%
 
G788311.3%
 
L785411.2%
 
P36615.2%
 
2022-01-09T13:35:20.456172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-01-09T13:35:20.503040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:20.565521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
F5053172.3%
 
G788311.3%
 
L785411.2%
 
P36615.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter69929100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F5053172.3%
 
G788311.3%
 
L785411.2%
 
P36615.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin69929100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
F5053172.3%
 
G788311.3%
 
L785411.2%
 
P36615.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII69929100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
F5053172.3%
 
G788311.3%
 
L785411.2%
 
P36615.2%
 

Unnamed: 34
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing69929
Missing (%)100.0%
Memory size546.4 KiB

Interactions

2022-01-09T13:34:48.836360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:34:48.929312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-01-09T13:35:03.275892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:03.353998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:03.447726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:03.525832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:03.619561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:03.697666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:03.781526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:03.875254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:03.984602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.062709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.156437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.250166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.343893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.437625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.531790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.625518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.719250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.812976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:04.906706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.000429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.078538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.172264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.265991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.359719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.453444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.547174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.625288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.719007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.812739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:05.906463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.015817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.093920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.187650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.296996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.390724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.468831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.562559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.656287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.750329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.844055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:06.937783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.031511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.115544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.209272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.302997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.396734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.490456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.584183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.677908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.771640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.865367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:07.959094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.037203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.130929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.209034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.302760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.380869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.474598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.733050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.808539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.902269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:08.980372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.074101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.152207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.245935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.339663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.417775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.511861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.589966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.668073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.746181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.839910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:09.918014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.011743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.089849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.167955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.261683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.339792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.433516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.511628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.605352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.683457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.777185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.855291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:10.949019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.027126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.105234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.183338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.277066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.355173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.433280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.511386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.605115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.683220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.777204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.855311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:11.933416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:12.027145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:12.110915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:12.204643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:12.283038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-09T13:35:20.643632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-09T13:35:20.815464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-09T13:35:20.987296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-09T13:35:21.159130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-01-09T13:35:21.299722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-01-09T13:35:12.652724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:13.423568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T13:35:13.820188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

PASSENGERSFREIGHTMAILDISTANCEUNIQUE_CARRIERAIRLINE_IDUNIQUE_CARRIER_NAMEUNIQUE_CARRIER_ENTITYREGIONCARRIERCARRIER_NAMECARRIER_GROUPCARRIER_GROUP_NEWORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGIN_CITY_MARKET_IDORIGINORIGIN_CITY_NAMEORIGIN_COUNTRYORIGIN_COUNTRY_NAMEORIGIN_WACDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEST_CITY_MARKET_IDDESTDEST_CITY_NAMEDEST_COUNTRYDEST_COUNTRY_NAMEDEST_WACYEARQUARTERMONTHDISTANCE_GROUPCLASSUnnamed: 34
00.01350.00.0136.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3311898118980231898GFKGrand Forks, NDUSUnited States6616232162320236232YWGWinnipeg, CanadaCACanada9262016131GNaN
10.01249813.00.0746.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3312339123390432337INDIndianapolis, INUSUnited States4216128161280336083YMXMontreal, CanadaCACanada9412016132GNaN
20.01735306.00.0440.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3312339123390432337INDIndianapolis, INUSUnited States4216271162710236106YYZToronto, CanadaCACanada9362016131GNaN
30.0889478.00.01706.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3313244132440233244MEMMemphis, TNUSUnited States5416042160420236039YEGEdmonton, CanadaCACanada9162016134GNaN
40.02155437.00.01113.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3313244132440233244MEMMemphis, TNUSUnited States5416128161280336083YMXMontreal, CanadaCACanada9412016133GNaN
50.01737431.00.01944.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3313244132440233244MEMMemphis, TNUSUnited States5416229162290231215YVRVancouver, CanadaCACanada9062016134GNaN
60.0985884.00.01090.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3313244132440233244MEMMemphis, TNUSUnited States5416232162320236232YWGWinnipeg, CanadaCACanada9262016133GNaN
70.01486385.00.01633.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3313244132440233244MEMMemphis, TNUSUnited States5416257162570330863YYCCalgary, CanadaCACanada9162016134GNaN
80.05005038.00.0812.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3313244132440233244MEMMemphis, TNUSUnited States5416271162710236106YYZToronto, CanadaCACanada9362016132GNaN
90.0300613.00.01706.0FX20107Federal Express Corporation06200DFXFederal Express Corporation3316042160420236039YEGEdmonton, CanadaCACanada91613244132440233244MEMMemphis, TNUSUnited States542016134GNaN

Last rows

PASSENGERSFREIGHTMAILDISTANCEUNIQUE_CARRIERAIRLINE_IDUNIQUE_CARRIER_NAMEUNIQUE_CARRIER_ENTITYREGIONCARRIERCARRIER_NAMECARRIER_GROUPCARRIER_GROUP_NEWORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGIN_CITY_MARKET_IDORIGINORIGIN_CITY_NAMEORIGIN_COUNTRYORIGIN_COUNTRY_NAMEORIGIN_WACDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEST_CITY_MARKET_IDDESTDEST_CITY_NAMEDEST_COUNTRYDEST_COUNTRY_NAMEDEST_WACYEARQUARTERMONTHDISTANCE_GROUPCLASSUnnamed: 34
6991956463.02953636.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012478124780331703JFKNew York, NYUSUnited States2212972129720430730LHRLondon, United KingdomGBUnited Kingdom4932016247FNaN
6992056784.04812982.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012972129720430730LHRLondon, United KingdomGBUnited Kingdom49312478124780331703JFKNew York, NYUSUnited States222016267FNaN
6992156995.02843005.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012478124780331703JFKNew York, NYUSUnited States2212972129720430730LHRLondon, United KingdomGBUnited Kingdom4932016387FNaN
6992257404.02593994.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012478124780331703JFKNew York, NYUSUnited States2212972129720430730LHRLondon, United KingdomGBUnited Kingdom4932016397FNaN
6992358108.04742021.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012972129720430730LHRLondon, United KingdomGBUnited Kingdom49312478124780331703JFKNew York, NYUSUnited States2220164107FNaN
6992458192.04995617.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012972129720430730LHRLondon, United KingdomGBUnited Kingdom49312478124780331703JFKNew York, NYUSUnited States222016257FNaN
6992559775.02643315.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012478124780331703JFKNew York, NYUSUnited States2212972129720430730LHRLondon, United KingdomGBUnited Kingdom4932016257FNaN
6992660485.02719677.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012478124780331703JFKNew York, NYUSUnited States2212972129720430730LHRLondon, United KingdomGBUnited Kingdom4932016267FNaN
6992761057.04903648.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012972129720430730LHRLondon, United KingdomGBUnited Kingdom49312478124780331703JFKNew York, NYUSUnited States222016377FNaN
6992861804.04272444.00.03451.0BA19540British Airways Plc9493EIBABritish Airways Plc0012972129720430730LHRLondon, United KingdomGBUnited Kingdom49312478124780331703JFKNew York, NYUSUnited States222016387FNaN